جست‌وجوی استاد و پژوهشگر

Hanna Vehkamäkiمشاهده پروفایل

استاد

• Atmospheric Science
• Physical Chemistry
• Computational Chemistry
• +۷ مورد دیگر

Hanna Vehkamäki is a Professor at the Faculty of Science, University of Helsinki , and Vice Dean responsible for well-being, equality, bilingual affairs, and facilities/safety. She leads the Academy of Finland Center of Excellence VILMA (2022-2029) focused on molecular-level atmospheric transformations. Field of Science: Physical Sciences Email: hanna.vehkamaki@helsinki.fi Address: P.O. Box 64, Gustaf Hällströmin katu 2, 00014 Helsinki Her research bridges atmospheric science , physical chemistry , and computational modeling , with a focus on molecular cluster dynamics, ion-induced nucleation, and aerosol-cloud-climate interactions. She develops tools for molecular-level atmospheric simulations and machine learning applications in predicting particle formation. Recent publications highlight her work on APi-ToF mass spectrometer optimization , α-pinene ozonolysis mechanisms , and alkylammonium ion mobility . Her projects include VILMA (Virtual laboratory for molecular-level atmospheric transformations) and Atmospheric Mathematics . Scientific Awards : Finnish Aerosol Research Foundation Distinguished Scientist Award (2014) Magnus Ehrnrooth Foundation award (2010) Suomen Valkoisen Ruusun I luokan ritarimerkki (2022) NOSA Aerosologist Award (2014) University of Helsinki Maikki Friberg award (2015) She supervises Master’s/PhD theses (e.g., hydration layer simulations on K-feldspar) and participates in international conferences (e.g., ISSPIC XVIII, Gordon Research Seminar). Her grants include the Academy of Finland Center of Excellence and the Jane and Aatos Erkko Foundation project RESTART.

Rafael Pereira Piresمشاهده پروفایل

مدرس

• Distributed Systems
• Machine Learning
• Privacy-Preserving Technologies
• +۳ مورد دیگر

Rafael Pereira Pires is a Lecturer and researcher at École polytechnique fédérale de Lausanne (EPFL) , affiliated with the Scalable Computing Systems Laboratory (SACS) and IC-SIN units. His research focuses on systems solutions at the intersection of privacy, efficiency, and machine learning in distributed environments. Education PhD in Computer Science (2019, University of Neuchâtel, Switzerland) Professional Master in Mechatronics (2014, IFSC, Brazil) Master in Computer Science (2009, UFSC, Brazil) His work explores privacy-preserving decentralized learning , trusted execution environments , and resource-efficient distributed systems . Recent publications address techniques like model fragmentation, approximate caching, and secure aggregation in decentralized learning contexts. Key trends in his 2023-2025 publications include: Advancements in federated learning and Mixture-of-Experts (MoE) models Applications of Trusted Execution Environments (SGX) to decentralized systems Optimization techniques for energy-aware and low-cost learning Scientific recognition includes the 2019 Léon Du Pasquier et Louis Perrier award for his PhD thesis. He has contributed to open-source tools like DecentralizePy and served as reviewer/PC member for top conferences including NeurIPS , Middleware , and ICDCS .

• Nonlinear Dynamics
• Structural Dynamics
• System Identification
• +۳ مورد دیگر

Dario Anastasio is a Fixed-term Assistant Professor in the Department of Mechanical and Aerospace Engineering (DIMEAS) at Politecnico di Torino. He is affiliated with the College of Mechanical, Aerospace, and Automotive Engineering and actively contributes to both teaching and research activities in mechanical engineering. Dr. Anastasio's research focuses on several key areas within mechanical engineering and dynamics. His primary interests include: Nonlinear dynamics and structural dynamics System identification and modal analysis Energy harvesting, particularly vibration energy harvesting Dynamics of mechanical systems with nonlinear characteristics Pantograph-catenary dynamic interaction in railway systems His work spans both theoretical modeling and experimental validation, with particular emphasis on negative stiffness oscillators, railway contact line dynamics, and nonlinear system identification techniques. Dr. Anastasio applies advanced methodologies including subspace identification, Bayesian model selection, and signal processing to solve complex mechanical engineering problems related to vibration analysis and structural dynamics. Analysis of Dr. Anastasio's publications reveals a strong focus on nonlinear dynamics, particularly in mechanical systems with complex behaviors. His research consistently bridges theoretical modeling with experimental validation across multiple domains including railway systems, energy harvesting devices, and nonlinear oscillators. The publications demonstrate progression from fundamental nonlinear dynamics research toward practical applications in railway engineering and vibration-based energy harvesting systems. Dr. Anastasio has received notable recognition for his work: Quality Award 2019 conferred by Politecnico di Torino, Italy (2020) Dr. Anastasio actively contributes to the academic community through extensive teaching activities across multiple programs. He serves as a Teaching Assistant for "Dynamics and Identification of Nonlinear Systems" and as a Course Collaborator for "Dynamics of Mechanical Systems" and "Vibration Mechanics" at both Master's and Bachelor's levels. His teaching spans from 2019/20 through the upcoming 2025/26 academic year, demonstrating his ongoing commitment to mechanical engineering education. Additionally, he contributes to PhD-level instruction in "Rotordynamics of High-Speed Rotating Machinery." Dr. Anastasio is a key member of the "Dynamics of mechanical systems and identification" research group within DIMEAS. His research integrates multiple ERC sectors including Mechanical and manufacturing engineering, ODE and dynamical systems, Signal processing, and Simulation engineering and modelling. His work aligns with Sustainable Development Goals 7 (Affordable and clean energy) and 9 (Industry, Innovation, and Infrastructure).

Bhaswar B. Bhattacharyaمشاهده پروفایل

دانشیار

• nonparametric statistics
• distribution-free inference
• networks analysis
• +۴ مورد دیگر

Bhaswar B. Bhattacharya is an Associate Professor of Statistics and Data Science at The Wharton School of the University of Pennsylvania, with a secondary appointment in the Department of Mathematics. His research spans several interconnected areas at the intersection of statistics, probability, and computational geometry. Dr. Bhattacharya received his Ph.D. in Statistics from Stanford University in 2016 under the supervision of Persi Diaconis. Prior to that, he earned both his Bachelor of Statistics (2009) and Master of Statistics (2011) from the Indian Statistical Institute in Kolkata. His research interests focus on three main pillars: nonparametric statistics (including distribution-free inference, nearest-neighbor methods, and inference on networks), combinatorial probability (covering counting problems in random graphs, random colorings, and graph limit theories), and discrete and computational geometry (including facility location problems, Voronoi games, and geometric Ramsey problems). His work often bridges theoretical developments with practical applications in network analysis, statistical learning, and geometric optimization. Recent publications demonstrate a strong trajectory in developing distribution-free methods for network analysis, with significant contributions to understanding fluctuations in graphon-based random graphs and developing optimal tests for inhomogeneous random graph models. His work also shows increasing focus on higher-order network structures through hypergraph models and applications to real-world problems like vaccination site optimization. NSF Career Award (2021-2026) Alfred P. Sloan Research Fellowship (2021) Probability Dissertation Award, Stanford University (2016) Sabyasachi Roy Memorial Gold Medal for best master's thesis, Indian Statistical Institute (2009-2011) Dr. Bhattacharya teaches advanced courses in mathematical statistics at both undergraduate and graduate levels at Wharton. His research program involves collaborations across multiple institutions and disciplines, with recent work applying statistical methods to public health challenges such as optimizing vaccination site locations. He maintains active research collaborations with colleagues in statistics, computer science, and applied mathematics departments.

• Electrical Engineering
• Computer Engineering
• Wireless Sensor Networks
• +۵ مورد دیگر

Mahmut Tenruh is an Associate Professor at Muğla Sıtkı Koçman University, Faculty of Engineering, Department of Electrical and Electronics Engineering. He holds a bachelor's degree from Gazi University and a Ph.D. from the University of Sussex. His research focuses on wireless sensor networks, CAN protocols, embedded systems, and renewable energy applications. 2024: Performance analysis of photovoltaic systems in Yemen 2021: Robotics control systems and intelligent vehicle suspension design 2019: Time-triggered CAN FD protocol for real-time distributed control 2015-2014: Network modeling, solar data tracking, and scheduling optimization His work has been cited 23 times across various publications. He has supervised over 20 graduate theses and led multiple TÜBİTAK-funded projects, including remote pool control systems and wireless security solutions. TÜBİTAK Scientific Award (2011) Muğla Sıtkı Koçman University Scientific Award (2011)

• High-performance computing
• Graph algorithms
• Graph machine learning
• +۱ مورد دیگر

Ariful Azad serves as an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University, where he leads research at the intersection of high-performance computing and graph analytics. His work focuses on developing scalable algorithms for graph machine learning with applications in bioinformatics and security informatics. Educational Background: Ph.D. in Computer Science, Purdue University (2014) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2006) Research Focus: Dr. Azad specializes in high-performance graph algorithms , particularly for distributed-memory systems. His pioneering work includes the Combinatorial BLAS library and novel approaches for graph neural networks (GNNs), with emphasis on explainability through Shapley values and optimization of sparse matrix operations. His bioinformatics research tackles large-scale metagenomics challenges through projects like Exabiome. Publication Trends: Recent publications (2023-2025) reveal three dominant themes: (1) Scalable GNN explanation frameworks using distributed Shapley values, (2) High-performance sparse linear algebra for graph embeddings and knowledge graphs, and (3) Bioinformatics applications in metagenomics and network alignment. His work consistently bridges theoretical algorithm development with practical implementations for exascale systems. Scientific Recognition: NSF CAREER Award (2024) for foundational contributions to scalable graph algorithms Indiana University Trustee's Teaching Award (2024) U.S. Department of Energy Early Career Award (2021) Research Leadership: As principal investigator for multiple federal grants, Dr. Azad directs projects advancing graph analytics at extreme scales. His work on Weapons of Mass Destruction knowledge graphs demonstrates applied security research, while Exabiome represents significant contributions to computational biology. He actively develops open-source tools like PLANETALIGN for network analysis benchmarking, fostering reproducibility in computational science.

Ulrike Pestel-Schillerمشاهده پروفایل

پژوهشگر

• Hyperspectral Imaging
• Image Processing
• Video Coding
• +۲ مورد دیگر

Dr. Ulrike Pestel-Schiller is a researcher at the Institute for Information Processing, Leibniz University Hannover, Germany, where she has been employed since 1996. Her work focuses on hyperspectral and Synthetic Aperture Radar (SAR) image processing, coding, and evaluation, with significant contributions to remote sensing applications. She actively supervises bachelor's and master's theses in these fields. Her academic background includes: Electrical Engineering and Communications Engineering studies at University of Hannover Dipl.-Ing. (Master's equivalent) awarded in 1989 Dr.-Ing. (Doctorate) completed in 1997 with dissertation on filter bank optimization for subband coding Her research centers on hyperspectral image data processing, coding efficiency, and usability evaluation for human interpreters. She investigates how compression techniques (HEVC, JPEG) impact SAR image usability, often finding counterintuitive results where compression improves interpretability. Recent work integrates deep learning, particularly CNNs, for spectral-spatial analysis in hyperspectral data and fruit classification. Her early career focused on HDTV video coding standards development. Analysis of her publication trends reveals a clear evolution from foundational HDTV subband coding research (1990s) to contemporary hyperspectral/SAR applications. A dominant theme is human-centered evaluation of compressed imagery, with 70% of her 2018-2023 publications examining interpreter performance. She increasingly employs deep learning for band selection and semantic segmentation, while maintaining core expertise in image compression algorithms. No scientific awards were documented in the source material. Dr. Pestel-Schiller supervises undergraduate and graduate theses in hyperspectral/SAR processing but no specific grant funding or formal advising records were provided. Her research appears institutionally supported through the Institute for Information Processing. The Institute for Information Processing serves as her primary research base, collaborating on projects involving drone remote sensing, VideoSAR stabilization, and hyperspectral band optimization. Current work emphasizes practical applications where image compression directly impacts interpreter effectiveness in remote sensing scenarios.

Chen Panمشاهده پروفایل

استادیار

• Energy-Harvesting Embedded Systems
• Internet of Things (IoT)
• Low-Power Computing
• +۵ مورد دیگر

Chen Pan is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio's Klesse College of Engineering and Integrated Design. His research focuses on energy-harvesting embedded systems, low-power computing, and IoT network optimization through machine learning techniques. Ph.D. from University of Pittsburgh Specializes in transient computing for batteryless devices Develops reinforcement learning solutions for UAV-assisted IoT systems Chen's recent publications emphasize energy-aware scheduling, non-volatile memory optimization, and sustainable communication protocols. His work intersects spatiotemporal modeling, fault tolerance, and resource-constrained AI execution across heterogeneous architectures.

• Geophysics
• Machine Learning
• Joint Inversion
• +۷ مورد دیگر

Jiajia Sun is an Associate Professor of Geophysics in the Department of Earth and Atmospheric Sciences at the University of Houston. Her research focuses on advancing subsurface imaging, uncertainty quantification, and mineral exploration through interdisciplinary approaches combining geophysics, machine learning, and computer vision. Education : PhD in Geophysics (2015, Colorado School of Mines); BS in Geophysics (2008, China University of Geosciences, Wuhan). Research Interests : Jiajia specializes in deep learning for geophysical inversion, multi-physics data integration, and probabilistic geological modeling. Her work leverages computational resources like GPUs and clusters to solve inverse problems and tackle magnetic remanence challenges. Recent Publications : Her research includes applying Bayesian frameworks, deep generative models, and joint inversion algorithms to airborne geophysics for critical mineral mapping and hydrogen reservoir detection. She emphasizes open-source tools like SimPEG for reproducibility. Awards : J. Clarence Karcher Award (SEG) Advising & Collaborations : She mentors PhD students in geophysics and collaborates with institutions like Amazon’s Generative AI Innovation Center, Stanford University, and University College Dublin. Her team also tests drones and magnetometers at the UH Coastal Center.

• Sustainable Tourism
• Transportation Planning
• Public Participation GIS
• +۳ مورد دیگر

Beatrice Waleghwa is a Doctoral Student at Mid Sweden University, affiliated with the Department of Economics, Geography, Law and Tourism (EJT) and ETOUR research center. Her research focuses on sustainable tourism mobility, particularly in rural contexts. Research Interests: Sustainable transportation planning Public Participation GIS (PPGIS) Rural tourism development Spatial analysis for tourism Her work connects tourism studies with transportation sustainability, focusing on Nordic destinations. Recent publications include journal articles, book chapters, and conference papers investigating car dependency, shared mobility, and GIS applications in tourism planning. Contact: Email: beatrice.waleghwa@miun.se

Eran Halperinمشاهده پروفایل

استاد

• Computational Biology
• Genomics
• Machine Learning in Medicine
• +۴ مورد دیگر

Dr. Eran Halperin is a Professor at the University of California, Los Angeles, affiliated with the School of Engineering (Computer Science Department) and the School of Medicine (Human Genetics, Computational Medicine, Anesthesiology). His research bridges computational biology, genomics, and machine learning, with a focus on developing statistical methods to analyze big medical data for disease prediction and treatment. Developed open-source software tools like FEAST, ReFACTor, and Bisque Recipients of prestigious awards including Rothschild Fellowship and ISCB Fellow (2021) Research Interests: Computational Genomics: Applying machine learning to genomic data (methylation, RNA expression) for disease understanding. Machine Learning in Medicine: Creating deep learning architectures for ophthalmology, anesthesiology, and acute care applications. Medical Data Science: Integrating electronic health records with genomic datasets for predictive modeling. Scientific Recognition: Rothschild Fellowship Technion-Juludan Research Prize Krill Prize in Science Elected ISCB Fellow (2021) Dr. Halperin's lab collaborates across disciplines, utilizing software platforms such as GLINT (methylation analysis) and MTV-LMM (microbiome prediction). His work has received funding from NIH, NSF, and international foundations.

• Machine Learning
• Artificial Intelligence
• Stochastic Differential Equations
• +۳ مورد دیگر

Prakhar Verma is a Doctoral Researcher at Aalto University, specializing in machine learning and probabilistic modeling. His work focuses on Gaussian processes, stochastic differential equations, and their applications in sequential learning and Bayesian optimization. Research Interests: Probabilistic modeling with Gaussian processes Scalable inference for stochastic differential equations Bayesian optimization and active learning techniques Sequential learning with memory-based approaches Mathematical modeling of continuous-time systems Computational statistics for high-dimensional data Collaborations: He collaborates with researchers like Arno Solin, Vincent Adam, Paul E. Chang, Ti John, and Victor Picheny. His publications span conferences such as NeurIPS, ICML, and AISTATS.

• Photonics
• Fiber Optic Sensors
• High-Power Lasers
• +۱۱ مورد دیگر

Guido Perrone is a Tenured Associate Professor at the Department of Electronics and Telecommunications (DET) , Politecnico di Torino, with a focus on Photonics, Fiber Optic Sensors, and High-Power Lasers. His research bridges industrial and biomedical applications, particularly additive manufacturing and laser therapy, aligning with SDG goals like Good Health, Industry Innovation, and Clean Water. Research Interests : Photonics, Fiber Optic Sensors, High-Power Lasers, Biomedical Applications, Additive Manufacturing, Electromagnetic Fields, Signal Processing, and Machine Learning. Projects : Led competitive grants including DESIGN (2024–2028), Yellow-FLiCkEr (2022–2025), and STELLAR (2021–2022) as Scientific Director/Manager. Teaching : Course instructor for Photonics , Industrial Photonics , and Passive Optical Components at undergraduate and graduate levels. Grants & Patents : Supervised >10 research projects and holds 2 patents (national/international) on optical transducers. Students : Mentored 4 PhD candidates in laser and sensor technologies.

René Vidalمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Biomedical Data Science
• +۳ مورد دیگر

René Vidal is the Rachleff & Penn Integrates Knowledge (PIK) University Professor at the University of Pennsylvania, with appointments in the Departments of Electrical and Systems Engineering, Radiology, Computer and Information Science, and Statistics and Data Science. He also serves as Director of the Center for Innovation in Data Engineering and Science (IDEAS) and the NSF-Simons Collaboration on the Mathematical Foundations of Deep Learning (THEORINET). A dual faculty member at Johns Hopkins University in Biomedical Engineering, Computer Science, and other departments, Vidal is an Amazon Scholar and Affiliated Chief Scientist at NORCE. PhD, Electrical Engineering and Computer Sciences, University of California, Berkeley (2003) M.S., Electrical Engineering, University of California, Berkeley (2000) B.S. (valedictorian), Electrical Engineering, Pontificia Universidad Catolica de Chile (1997) His research spans the mathematical foundations of deep learning, focusing on non-convex optimization , learning dynamics , and overparametrization . Key contributions include Sparse Subspace Clustering , Kernel GPCA , and Low-Rank Matrix Factorization , with applications in autism diagnosis , robotic surgery , and cardiac imaging . Recent work explores continual learning , adversarial robustness , and trustworthy AI in biomedical contexts. His 15 most recent publications highlight advances in medical imaging , language models , and robust computer vision , spanning topics from chest X-ray analysis to motor imitation tasks in autism . Articles like Geometric Analysis of Nonlinear Manifold Clustering underscore his theoretical contributions, while works on KDA: Knowledge-Distilled Attacker and Conformal Information Pursuit address practical AI safety and interpretability. Scientific accolades include: 2022 ACM Fellow 2021 IEEE McCluskey Technical Achievement Award 2017 Jean D’Alembert Fellowship 2012 J.K. Aggarwal Prize 2009 Sloan Research Fellow 2005 NSF CAREER Award His lab mentors 11 current PhD students across Johns Hopkins and University of Pennsylvania , with alumni contributing to institutions like Meta , Amazon , and GE Research . Vidal’s interdisciplinary work bridges mathematics , engineering , and healthcare , supported by grants from the DoD , NSF , and ONR .

• Time Series Analysis
• Energy Analytics
• Statistical Clustering
• +۳ مورد دیگر

Dr. Andrés Modesto Alonso is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid, affiliated with the Energy Analytics research group and Flores de Lemus Institute. His work spans computer science, economics, and statistics through advanced time series analysis and energy forecasting methodologies. Primary affiliation: Department of Statistics, UC3M Research groups: Energy Analytics, Flores de Lemus Institute His research focuses on time series analysis , energy forecasting , and statistical clustering with applications in electricity markets, smart grids, and environmental data. Recent publications emphasize deep learning models for energy prediction, dynamic factor models, and market-based distance metrics. Scientific output trends show 15 recent articles (2018-2024) covering topics like: Electricity market price forecasting Smart grid optimization through clustering Adaptive control charts for industrial processes Precision matrix estimation in high-dimensional statistics Extreme value analysis for environmental monitoring Dr. Alonso has supervised multiple theses on time series modeling and classification techniques, while collaborating on grants related to stochastic optimization and responsible AI applications in economic forecasting.